Title: Review on sentiment analysis of movie reviews using machine learning techniques based on data available on Twitter

Authors: Dharmendra Dangi; Amit Bhagat; Jeetendra Kumar Gupta

Addresses: Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462003, India; Department of Computer Science and Application, Atal Bihari Vajpayee Vishwavidyalaya, Bilaspur, India ' Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462003, India; Department of Computer Science and Application, Atal Bihari Vajpayee Vishwavidyalaya, Bilaspur, India ' Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462003, India; Department of Computer Science and Application, Atal Bihari Vajpayee Vishwavidyalaya, Bilaspur, India

Abstract: Opinion mining or sentiment analysis is the study to extract useful information from the given datasets like tweets on Twitter or opinions of people on other social blogs or portals related to a particular topic. Sentiment analysis aims to predict the type of opinion like positive, somewhat positive, or negative somewhat negative and neutral. Sentiment analysis based on machine learning techniques has more importance as it gives better outputs. The study of these kinds of datasets with the help of machine learning techniques can be used in many different forms like to make predictions, to study the patterns, to analyse the sentiments, to study the reviews the movies, to predict the way stock market may behave. Data available on microblogging sites like Twitter have certain hidden indications which are useful to solve many research problems. This article is the review article that will highlight some recent studies in the field of sentiment analysis based on the movie review available on websites like Twitter.

Keywords: machine learning; sentiment analysis; positive; negative; Twitter.

DOI: 10.1504/IJESMS.2024.140808

International Journal of Engineering Systems Modelling and Simulation, 2024 Vol.15 No.5, pp.253 - 259

Received: 10 May 2021
Accepted: 26 May 2022
Published online: 03 Sep 2024 *

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